Enterprise consolidates 200 apps onto single self-hosted LLM via multi-axis expert merging
An enterprise has solved a critical infrastructure problem by consolidating 200+ internal applications onto a single self-hosted LLM, rather than maintaining a sprawling fleet of models. The approach identifies production bottlenecks across three dimensions (instruction following, function-calling, task distribution), then trains specialized GRPO experts for each axis before merging them via SLERP. This addresses a real tension in corporate AI adoption: data residency requirements force on-premise hosting, but model proliferation fragments GPU budgets. The technique demonstrates how production telemetry can drive targeted post-training improvements without cross-domain reward conflicts, offering a template for enterprises facing similar consolidation pressures.
Modelwire context
Analyst takeThe paper doesn't just show that you can merge specialized models; it demonstrates that production bottleneck data can drive post-training without creating reward conflicts across domains. That's the constraint most enterprises haven't solved yet.
This consolidation pattern mirrors what we've seen in professional services and agriculture. Gilbert + Tobin's governance framework and John Deere's domain-specific embedding both faced the same underlying tension: regulatory or data-residency requirements force on-premise hosting, but that infrastructure cost pressures companies to unify rather than proliferate. The difference here is methodological: instead of deploying a single general model (which underperforms on specialized tasks), this approach uses production traffic to identify which axes matter most, then trains targeted experts and merges them. It's the missing link between the operational necessity we documented and the technical execution. The document VLM paper from the same day also addresses this (cost-quality parity through specialized fine-tuning on production data), but that's a single-task system; this one handles 200+ applications.
If other enterprises publish similar consolidation results within the next 12 months using the same GRPO-plus-SLERP approach, that signals the method is reproducible and will likely become standard practice. If they don't, the bottleneck is probably in identifying which production signals actually matter for their specific application mix, not in the merging technique itself.
Coverage we drew on
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MentionsGRPO · SLERP · LLM
Modelwire Editorial
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.